As an urban-scale sensing network that collects real-time environmental data in cities, the AoT initiative exemplifies this trend. This is an initiative led by Charlie Catlett and the researchers from the Urban Center for Computation and Data, a joint initiative of the Argonne National Laboratory and the University of Chicago. Launched in 2016 and currently implemented in Chicago, the data collected through this initiative is open and free to the public.
Our project seeks to evaluate the level of reliability of the data collected by the AoT network. We provide a method to numerically score daily network data reliability for the network in terms of different data parameters. Based on this score metric, we hope that planners could easily identify segments of the big AoT dataset for their relevant analysis. We also hope the transparent evaluation factors and scores behind this data reliability analysis could promote a more informed use of data in the increasingly data-driven planning process. This application is to be useful in improving research efficiency, considering the increasingly large stores of sensor data available - planners will be able to scope the spatial and temporal scale of their research according to where and when reliable segments of the data is available, instead of having to explore many different datasets to finalise a suitable scope of analysis.
This website should be used following the steps:
(1) Select a parameter
(2) Select a date
(*Because of the data collection, please select the date range between December 1st 2018 - December 31st 2018: suggest start from December 15st 2018)
(3) Select raw value or imputed value
(4) Select apply button